Gut-directed hypnotherapy in children with irritable bowel syndrome or functional abdominal pain (syndrome): A randomized controlled trial on self exercises at home using CD versus individual therapy by qualified therapists

Background: Irritable bowel syndrome (IBS) and functional abdominal pain (syndrome) (FAP(S)) are common pediatric disorders, characterized by chronic or recurrent abdominal pain. Treatment is challenging, especially in children with persisting symptoms. Gut-directed hypnotherapy (HT) performed by a therapist has been shown to be effective in these children, but is still unavailable to many children due to costs, a lack of qualified child-hypnotherapists and because it requires a significant investment of time by child and parent(s). Home-based hypnotherapy by means of exercises on CD has been shown effective as well, and has potential benefits, such as lower costs and less time investment. The aim of this randomized controlled trial (RCT) is to compare cost-effectiveness of individual HT performed by a qualified therapist with HT by means of CD recorded self-exercises at home in children with IBS or FAP(S).Methods/Design: 260 children, aged 8-18 years with IBS or FAP(S) according to Rome III criteria are included in this currently conducted RCT with a follow-up period of one year. Children are randomized to either 6 sessions of individual HT given by a qualified therapist over a 3-month period or HT through self-exercises at home with CD for 3 months.The primary outcome is the proportion of patients in which treatment is successful at the end of treatment and after one year follow-up. Treatment success is defined as at least 50% reduction in both abdominal pain frequency and intensity scores. Secondary outcomes include adequate relief, cost-effectiveness an

Dual-camera 3D head tracking for clinical infant monitoring

This paper presents a new algorithm for dual-camera 3D head tracking, intended for clinical infant monitoring. The paper includes a brief motivation with reference to the state-of-the-art in face-related image analysis. The proposed algorithm uses a clipped-ellipsoid head model and 3D head pose recovery by joint alignment of paired templates based on dense-HOG features. In the algorithm, template pairs are dynamically extracted and a limited number of template pairs are stored and re-used for drift reduction. We report experimental results on real-life videos of infants in bed in a hospital, captured in visual light as well as near-infrared light. Results show consistently good tracking behavior. For challenging video sequences, the mean tracking error in terms of endocanthion location error relative to the innercanthal distance remains below 30%. This error has proven to be sufficiently low for 3D head tracking to support infant face analysis. For this reason, the proposed algorithm is used successfully in an infant monitoring system under development

Surgery for refractory anterior cutaneous nerve entrapment syndrome (ACNES) in children.

Item does not contain fulltextBACKGROUND: Chronic abdominal pain (CAP) in children may be caused by entrapment of cutaneous branches of intercostal nerves (anterior cutaneous nerve entrapment syndrome, or ACNES). Local injection of anesthetics may offer relief, but pain is persistent in some children. This study is the first to describe the results of a 'cutaneous neurectomy' in children with refractory ACNES. METHODS: Chronic abdominal pain children with suspected ACNES refractory to conservative treatment received a cutaneous neurectomy in a day care setting. They were interviewed postoperatively using an adapted quality of life questionnaire (testing quality of life in children). RESULTS: All subjects (n = 6; median age, 15 years; range, 9-16 years) were previously healthy school-aged children without prior illness or earlier surgery. Each presented with intense abdominal pain and a positive Carnett sign. Blood, urine tests, and abdominal ultrasound investigations were normal. Delay in seeing a physician was 16 weeks, and school absence was 25 days. Before surgery, quality of life (pain, daily activities, and sports) was greatly diminished. After the neurectomy, all children were free of pain and had resumed their normal daily routine (follow-up at 6 months). CONCLUSIONS: The role of the abdominal wall as the source of childhood CAP is underestimated. Some children with CAP have ACNES. Children with refractory ACNES should be offered a cutaneous neurectomy, as this simple technique is effective in the short and long term.1 april 201

Video-based discomfort detection for infants using a Constrained Local Model

Automatic discomfort detection for infants is important in health care, since infants have no ability to express their discomfort. In this paper, we propose an automatic system for detecting and monitoring discomfort of infants based on video analysis. The system is based on supervised learning and classifies previously unseen infants from the testing set in a fully automated way. Our system consists of face detection and discomfort detection. For each frame, we first detect a face area by using a combination of a skin-color detector and a ViolaJones face detector, and then fit a face shape to the detected face area by using a Constrained Local Model (CLM). After that, we extract expression features by using Elongated Local Binary Pattern (ELBP) on a similarity-normalized appearance (SAPP), and classify expression features with a Support Vector Machine (SVM) for discomfort detection. The key contribution of our system is that it is infant independent and requires no prior knowledge about previously unseen infants. The face detector of the system has an accuracy of 81.5%. The system detects discomfort with an accuracy of 84.3%, a sensitivity of 82.4%, and specificity of 84.9% on the testing set containing videos of 11 infants

Video-based facial discomfort analysis for infants

Prematurely born infants receive special care in the Neonatal Intensive Care Unit (NICU), where various physiological parameters, such as heart rate, oxygen saturation and temperature are continuously monitored. However, there is no system for monitoring and interpreting their facial expressions, the most prominent discomfort indicator. In this paper, we present an experimental video monitoring system for automatic discomfort detection in infants' faces based on the analysis of their facial expressions. The proposed system uses an Active Appearance Model (AAM) to robustly track both the global motion of the newborn's face, as well as its inner features. The system detects discomfort by employing the AAM representations of the face on a frame-by-frame basis, using a Support Vector Machine (SVM) classifier. Three contributions increase the performance of the system. First, we extract several histogram-based texture descriptors to improve the AAM appearance representations. Second, we fuse the outputs of various individual SVM classifiers, which are trained on features with complementary qualities. Third, we improve the temporal behavior and stability of the discomfort detection by applying an averaging filter to the classification outputs. Additionally, for a higher robustness, we explore the effect of applying different image pre-processing algorithms for correcting illumination conditions and for image enhancement to evaluate possible detection improvements. The proposed system is evaluated in 15 videos of 8 infants, yielding a 0.98 AUC performance. As a bonus, the system offers monitoring of the infant's expressions when it is left unattended and it additionally provides objective judgment of discomfort

Robust discomfort detection for infants using an unsupervised roll estimation

Discomfort detection for infants is essential in the healthcare domain, since infants lack the ability to verbalize their pain and discomfort. In this paper, we propose a robust and generic discomfort detection for infants by exploiting a novel and efficient initialization method for facial landmark localization, using an unsupervised rollangle estimation. The roll-angle estimation is achieved by fitting a 1st-order B-spline model to facial features obtained from the scaled-normalized Laplacian of the Gaussian operator. The proposed method can be adopted both for daylight and infrared-light images and supports real-time implementation. Experimental results have shown that the proposed method improves the performance of discomfort detection by 6.0% and 4.2% for the AUC and AP using daylight images, together with 6.9% and 3.8% for infrared-light images, respectively

Enhanced face alignment using an unsupervised roll estimation initialization

We propose a novel and efficient initialization method for generalized facial landmark localization with an unsupervised roll-angle estimation based on B-spline models. We first show that the roll angle is crucial for an accurate landmark localization. Therefore, we develop an unsupervised roll-angle estimation by adopting a joint 1 st -order B-spline model, which is robust to intensity variations and generic for application to various face detectors. The method consists of three steps. First, the scaled-normalized Laplacian of Gaussian operator is applied to a bounding box generated by a face detector for extracting facial feature segments. Second, a joint 1 st -order B-spline model is fitted to the extracted facial feature segments, using an iterative optimization method. Finally, the roll angle is estimated through the aligned segments. We evaluate four state-of-the-art landmark localization schemes with the proposed roll-angle estimation initialization in the benchmark dataset. The proposed method boosts the performance of landmark localization in general, especially for cases with large head pose. Moreover, the proposed unsupervised roll-angle estimation method outperforms the standard supervised methods, such as random forest and support vector regression by 41.6% and 47.2%, respectively